{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9d71306f",
   "metadata": {},
   "source": [
    "Copyright (c) MONAI Consortium  \n",
    "Licensed under the Apache License, Version 2.0 (the \"License\");  \n",
    "you may not use this file except in compliance with the License.  \n",
    "You may obtain a copy of the License at  \n",
    "&nbsp;&nbsp;&nbsp;&nbsp;http://www.apache.org/licenses/LICENSE-2.0  \n",
    "Unless required by applicable law or agreed to in writing, software  \n",
    "distributed under the License is distributed on an \"AS IS\" BASIS,  \n",
    "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.  \n",
    "See the License for the specific language governing permissions and  \n",
    "limitations under the License.\n",
    "\n",
    "# Denoising Diffusion Probabilistic Models with MedNIST Dataset\n",
    "\n",
    "This tutorial illustrates how to use MONAI for training a denoising diffusion probabilistic model (DDPM)[1] to create\n",
    "synthetic 2D images.\n",
    "\n",
    "[1] - Ho et al. \"Denoising Diffusion Probabilistic Models\" https://arxiv.org/abs/2006.11239\n",
    "\n",
    "\n",
    "## Setup environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6aa3774e",
   "metadata": {},
   "outputs": [],
   "source": [
    "!python -c \"import monai\" || pip install -q \"monai-weekly[tqdm]\"\n",
    "!python -c \"import matplotlib\" || pip install -q matplotlib\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3154fee",
   "metadata": {},
   "source": [
    "## Setup imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dd62a552",
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MONAI version: 1.4.0rc6\n",
      "Numpy version: 1.26.4\n",
      "Pytorch version: 2.3.1+cu121\n",
      "MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False\n",
      "MONAI rev id: 6a0e1b043ba2890e1463fa49df76f66e56a68b08\n",
      "MONAI __file__: /home/<username>/miniconda3/envs/monai/lib/python3.11/site-packages/monai/__init__.py\n",
      "\n",
      "Optional dependencies:\n",
      "Pytorch Ignite version: 0.4.11\n",
      "ITK version: 5.4.0\n",
      "Nibabel version: 5.2.1\n",
      "scikit-image version: 0.23.2\n",
      "scipy version: 1.13.1\n",
      "Pillow version: 10.3.0\n",
      "Tensorboard version: 2.17.0\n",
      "gdown version: 5.2.0\n",
      "TorchVision version: 0.18.1+cu121\n",
      "tqdm version: 4.66.4\n",
      "lmdb version: 1.4.1\n",
      "psutil version: 5.9.0\n",
      "pandas version: 2.2.2\n",
      "einops version: 0.8.0\n",
      "transformers version: NOT INSTALLED or UNKNOWN VERSION.\n",
      "mlflow version: 2.14.0\n",
      "pynrrd version: 1.0.0\n",
      "clearml version: 1.16.2rc0\n",
      "\n",
      "For details about installing the optional dependencies, please visit:\n",
      "    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import shutil\n",
    "import tempfile\n",
    "import time\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "from monai import transforms\n",
    "from monai.apps import MedNISTDataset\n",
    "from monai.config import print_config\n",
    "from monai.data import CacheDataset, DataLoader\n",
    "from monai.utils import first, set_determinism\n",
    "from torch.amp import GradScaler, autocast\n",
    "from tqdm import tqdm\n",
    "\n",
    "from monai.inferers import DiffusionInferer\n",
    "from monai.networks.nets import DiffusionModelUNet\n",
    "from monai.networks.schedulers import DDPMScheduler\n",
    "\n",
    "print_config()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be99fa93",
   "metadata": {},
   "source": [
    "## Setup data directory\n",
    "\n",
    "You can specify a directory with the MONAI_DATA_DIRECTORY environment variable.\n",
    "\n",
    "This allows you to save results and reuse downloads.\n",
    "\n",
    "If not specified a temporary directory will be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8fc58c80",
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/tmp/tmp62w8dzsl\n"
     ]
    }
   ],
   "source": [
    "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n",
    "root_dir = tempfile.mkdtemp() if directory is None else directory\n",
    "print(root_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a36b12f0",
   "metadata": {},
   "source": [
    "## Set deterministic training for reproducibility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ad5a1948",
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "set_determinism(42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b41e37b3",
   "metadata": {},
   "source": [
    "## Setup MedNIST Dataset and training and validation dataloaders\n",
    "In this tutorial, we will train our models on the MedNIST dataset available on MONAI\n",
    "(https://docs.monai.io/en/stable/apps.html#monai.apps.MedNISTDataset). In order to train faster, we will select just\n",
    "one of the available classes (\"Hand\"), resulting in a training set with 7999 2D images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "65e1c200",
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024-09-02 16:39:54,821 - INFO - Downloaded: /tmp/tmp62w8dzsl/MedNIST.tar.gz\n",
      "2024-09-02 16:39:54,938 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n",
      "2024-09-02 16:39:54,940 - INFO - Writing into directory: /tmp/tmp62w8dzsl.\n"
     ]
    }
   ],
   "source": [
    "train_data = MedNISTDataset(root_dir=root_dir, section=\"training\", download=True, progress=False, seed=0)\n",
    "train_datalist = [{\"image\": item[\"image\"]} for item in train_data.data if item[\"class_name\"] == \"Hand\"]\n",
    "batch_size = 64\n",
    "num_workers = 4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d503ec9",
   "metadata": {},
   "source": [
    "Here we use transforms to augment the training dataset:\n",
    "\n",
    "1. `LoadImaged` loads the hands images from files.\n",
    "1. `EnsureChannelFirstd` ensures the original data to construct \"channel first\" shape.\n",
    "1. `ScaleIntensityRanged` extracts intensity range [0, 255] and scales to [0, 1].\n",
    "1. `RandAffined` efficiently performs rotate, scale, shear, translate, etc. together based on PyTorch affine transform."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e2f9bebd",
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading dataset: 100%|██████████| 7999/7999 [00:05<00:00, 1406.10it/s]\n"
     ]
    }
   ],
   "source": [
    "train_transforms = transforms.Compose(\n",
    "    [\n",
    "        transforms.LoadImaged(keys=[\"image\"]),\n",
    "        transforms.EnsureChannelFirstd(keys=[\"image\"]),\n",
    "        transforms.ScaleIntensityRanged(keys=[\"image\"], a_min=0.0, a_max=255.0, b_min=0.0, b_max=1.0, clip=True),\n",
    "        transforms.RandAffined(\n",
    "            keys=[\"image\"],\n",
    "            rotate_range=[(-np.pi / 36, np.pi / 36), (-np.pi / 36, np.pi / 36)],\n",
    "            translate_range=[(-1, 1), (-1, 1)],\n",
    "            scale_range=[(-0.05, 0.05), (-0.05, 0.05)],\n",
    "            spatial_size=[64, 64],\n",
    "            padding_mode=\"zeros\",\n",
    "            prob=0.5,\n",
    "        ),\n",
    "    ]\n",
    ")\n",
    "train_ds = CacheDataset(data=train_datalist, transform=train_transforms)\n",
    "train_loader = DataLoader(\n",
    "    train_ds, batch_size=batch_size, shuffle=True, num_workers=num_workers, persistent_workers=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "938318c2",
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024-09-02 16:40:26,432 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n",
      "2024-09-02 16:40:26,433 - INFO - File exists: /tmp/tmp62w8dzsl/MedNIST.tar.gz, skipped downloading.\n",
      "2024-09-02 16:40:26,435 - INFO - Non-empty folder exists in /tmp/tmp62w8dzsl/MedNIST, skipped extracting.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading dataset: 100%|██████████| 1005/1005 [00:00<00:00, 1478.76it/s]\n"
     ]
    }
   ],
   "source": [
    "val_data = MedNISTDataset(root_dir=root_dir, section=\"validation\", download=True, progress=False, seed=0)\n",
    "val_datalist = [{\"image\": item[\"image\"]} for item in val_data.data if item[\"class_name\"] == \"Hand\"]\n",
    "val_transforms = transforms.Compose(\n",
    "    [\n",
    "        transforms.LoadImaged(keys=[\"image\"]),\n",
    "        transforms.EnsureChannelFirstd(keys=[\"image\"]),\n",
    "        transforms.ScaleIntensityRanged(keys=[\"image\"], a_min=0.0, a_max=255.0, b_min=0.0, b_max=1.0, clip=True),\n",
    "    ]\n",
    ")\n",
    "val_ds = CacheDataset(data=val_datalist, transform=val_transforms)\n",
    "val_loader = DataLoader(val_ds, batch_size=batch_size, shuffle=False, num_workers=num_workers, persistent_workers=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a56a4e42",
   "metadata": {},
   "source": [
    "### Visualisation of the training images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b698f4f8",
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "batch shape: torch.Size([128, 1, 64, 64])\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "check_data = first(train_loader)\n",
    "print(f\"batch shape: {check_data['image'].shape}\")\n",
    "image_visualisation = torch.cat(\n",
    "    [check_data[\"image\"][0, 0], check_data[\"image\"][1, 0], check_data[\"image\"][2, 0], check_data[\"image\"][3, 0]], dim=1\n",
    ")\n",
    "plt.figure(\"training images\", (12, 6))\n",
    "plt.imshow(image_visualisation, vmin=0, vmax=1, cmap=\"gray\")\n",
    "plt.axis(\"off\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3090350",
   "metadata": {},
   "source": [
    "### Define network, scheduler, optimizer, and inferer\n",
    "At this step, we instantiate the MONAI components to create a DDPM, the UNET, the noise scheduler, and the inferer used for training and sampling. We are using\n",
    "the original DDPM scheduler containing 1000 timesteps in its Markov chain, and a 2D UNET with attention mechanisms\n",
    "in the 2nd and 3rd levels, each with 1 attention head."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2c52e4f4",
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    },
    "lines_to_next_cell": 0
   },
   "outputs": [],
   "source": [
    "device = torch.device(\"cuda\")\n",
    "num_train_timesteps = 1000\n",
    "\n",
    "model = DiffusionModelUNet(\n",
    "    spatial_dims=2,\n",
    "    in_channels=1,\n",
    "    out_channels=1,\n",
    "    channels=(128, 256, 256),\n",
    "    attention_levels=(False, True, True),\n",
    "    num_res_blocks=1,\n",
    "    num_head_channels=256,\n",
    ")\n",
    "model.to(device)\n",
    "\n",
    "scheduler = DDPMScheduler(num_train_timesteps=num_train_timesteps)\n",
    "\n",
    "optimizer = torch.optim.Adam(params=model.parameters(), lr=2.5e-5)\n",
    "\n",
    "inferer = DiffusionInferer(scheduler)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a316067",
   "metadata": {},
   "source": [
    "### Model training\n",
    "Here, we are training our model for 75 epochs (training time: ~50 minutes).\n",
    "\n",
    "If you would like to skip the training and use a pre-trained model instead, set `use_pretrained=True`. This model was trained using the code in `tutorials/generative/distributed_training/ddpm_training_ddp.py` of [GenerateModels](https://github.com/Project-MONAI/GenerativeModels)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0f697a13",
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    },
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 0:   0%|                                 | 0/63 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 0: 100%|███████████| 63/63 [00:29<00:00,  2.10it/s, loss=0.0187]\n",
      "Epoch 1: 100%|███████████| 63/63 [00:29<00:00,  2.11it/s, loss=0.0159]\n",
      "Epoch 2: 100%|███████████| 63/63 [00:30<00:00,  2.09it/s, loss=0.0154]\n",
      "Epoch 3: 100%|███████████| 63/63 [00:30<00:00,  2.09it/s, loss=0.0146]\n",
      "Epoch 4: 100%|███████████| 63/63 [00:30<00:00,  2.09it/s, loss=0.0139]\n",
      "...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 70: 100%|█████████| 63/63 [00:29<00:00,  2.11it/s, loss=0.00954]\n",
      "Epoch 71: 100%|█████████| 63/63 [00:29<00:00,  2.11it/s, loss=0.00901]\n",
      "Epoch 72: 100%|█████████| 63/63 [00:29<00:00,  2.11it/s, loss=0.00878]\n",
      "Epoch 73: 100%|█████████| 63/63 [00:29<00:00,  2.11it/s, loss=0.00934]\n",
      "Epoch 74: 100%|██████████| 63/63 [00:29<00:00,  2.11it/s, loss=0.0092]\n",
      "100%|██████████| 1000/1000 [00:11<00:00, 86.39it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train completed, total time: 2443.8157732486725.\n"
     ]
    }
   ],
   "source": [
    "use_pretrained = False\n",
    "\n",
    "if use_pretrained:\n",
    "    model = torch.hub.load(\"marksgraham/pretrained_generative_models:v0.2\", model=\"ddpm_2d\", verbose=True).to(device)\n",
    "else:\n",
    "    max_epochs = 75\n",
    "    val_interval = 5\n",
    "    epoch_loss_list = []\n",
    "    val_epoch_loss_list = []\n",
    "\n",
    "    scaler = GradScaler(\"cuda\")\n",
    "    total_start = time.time()\n",
    "    for epoch in range(max_epochs):\n",
    "        model.train()\n",
    "        epoch_loss = 0\n",
    "        progress_bar = tqdm(enumerate(train_loader), total=len(train_loader), ncols=70)\n",
    "        progress_bar.set_description(f\"Epoch {epoch}\")\n",
    "        for step, batch in progress_bar:\n",
    "            images = batch[\"image\"].to(device)\n",
    "            optimizer.zero_grad(set_to_none=True)\n",
    "\n",
    "            with autocast(device_type=\"cuda\", enabled=True):\n",
    "                # Generate random noise\n",
    "                noise = torch.randn_like(images).to(device)\n",
    "\n",
    "                # Create timesteps\n",
    "                timesteps = torch.randint(0, num_train_timesteps, (images.shape[0],), device=images.device).long()\n",
    "\n",
    "                # Get model prediction\n",
    "                noise_pred = inferer(inputs=images, diffusion_model=model, noise=noise, timesteps=timesteps)\n",
    "\n",
    "                loss = F.mse_loss(noise_pred.float(), noise.float())\n",
    "\n",
    "            scaler.scale(loss).backward()\n",
    "            scaler.step(optimizer)\n",
    "            scaler.update()\n",
    "\n",
    "            epoch_loss += loss.item()\n",
    "\n",
    "            progress_bar.set_postfix({\"loss\": epoch_loss / (step + 1)})\n",
    "        epoch_loss_list.append(epoch_loss / (step + 1))\n",
    "\n",
    "        if (epoch + 1) % val_interval == 0:\n",
    "            model.eval()\n",
    "            val_epoch_loss = 0\n",
    "            for step, batch in enumerate(val_loader):\n",
    "                images = batch[\"image\"].to(device)\n",
    "                with torch.no_grad(), autocast(device_type=\"cuda\", enabled=True):\n",
    "                    noise = torch.randn_like(images).to(device)\n",
    "                    timesteps = torch.randint(0, num_train_timesteps, (images.shape[0],), device=images.device).long()\n",
    "                    noise_pred = inferer(inputs=images, diffusion_model=model, noise=noise, timesteps=timesteps)\n",
    "                    val_loss = F.mse_loss(noise_pred.float(), noise.float())\n",
    "\n",
    "                val_epoch_loss += val_loss.item()\n",
    "                progress_bar.set_postfix({\"val_loss\": val_epoch_loss / (step + 1)})\n",
    "            val_epoch_loss_list.append(val_epoch_loss / (step + 1))\n",
    "\n",
    "            # Sampling image during training\n",
    "            noise = torch.randn((1, 1, 64, 64))\n",
    "            noise = noise.to(device)\n",
    "            scheduler.set_timesteps(num_inference_steps=1000)\n",
    "            with autocast(device_type=\"cuda\", enabled=True):\n",
    "                image = inferer.sample(input_noise=noise, diffusion_model=model, scheduler=scheduler)\n",
    "\n",
    "            plt.figure(figsize=(2, 2))\n",
    "            plt.imshow(image[0, 0].cpu(), vmin=0, vmax=1, cmap=\"gray\")\n",
    "            plt.tight_layout()\n",
    "            plt.axis(\"off\")\n",
    "            plt.show()\n",
    "\n",
    "    total_time = time.time() - total_start\n",
    "    print(f\"train completed, total time: {total_time}.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "057f0d69",
   "metadata": {},
   "source": [
    "### Learning curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2cdcda81",
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if not use_pretrained:\n",
    "    plt.title(\"Learning Curves\", fontsize=20)\n",
    "    plt.plot(np.linspace(1, max_epochs, max_epochs), epoch_loss_list, color=\"C0\", linewidth=2.0, label=\"Train\")\n",
    "    plt.plot(\n",
    "        np.linspace(val_interval, max_epochs, int(max_epochs / val_interval)),\n",
    "        val_epoch_loss_list,\n",
    "        color=\"C1\",\n",
    "        linewidth=2.0,\n",
    "        label=\"Validation\",\n",
    "    )\n",
    "    plt.yticks(fontsize=12)\n",
    "    plt.xticks(fontsize=12)\n",
    "    plt.xlabel(\"Epochs\", fontsize=16)\n",
    "    plt.ylabel(\"Loss\", fontsize=16)\n",
    "    plt.legend(prop={\"size\": 14})\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d61cffe",
   "metadata": {},
   "source": [
    "### Plotting sampling process along DDPM's Markov chain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1427e5d4",
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1000/1000 [00:11<00:00, 85.66it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.eval()\n",
    "noise = torch.randn((1, 1, 64, 64))\n",
    "noise = noise.to(device)\n",
    "scheduler.set_timesteps(num_inference_steps=1000)\n",
    "with autocast(device_type=\"cuda\", enabled=True):\n",
    "    image, intermediates = inferer.sample(\n",
    "        input_noise=noise, diffusion_model=model, scheduler=scheduler, save_intermediates=True, intermediate_steps=100\n",
    "    )\n",
    "\n",
    "chain = torch.cat(intermediates, dim=-1)\n",
    "\n",
    "plt.imshow(chain[0, 0].cpu(), vmin=0, vmax=1, cmap=\"gray\")\n",
    "plt.tight_layout()\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c45cead",
   "metadata": {},
   "source": [
    "### Cleanup data directory\n",
    "\n",
    "Remove directory if a temporary was used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bab2d719",
   "metadata": {},
   "outputs": [],
   "source": [
    "if directory is None:\n",
    "    shutil.rmtree(root_dir)"
   ]
  }
 ],
 "metadata": {
  "jupytext": {
   "formats": "ipynb,py:percent"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
